Topics: Facial recognition system, Biometrics, Face Pages: 54 (14192 words) Published: December 6, 2012
School of Computer Science and Information Technology

University of Nottingham

Orthoface for Recognition


Pose-Invariant Face Recognition

Voon Piao SIANG


A thesis in partial fulfilment of the requirement of University of Nottingham for the degree of Master of Philosophy

This copy of thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with the author and that no information derived from it may be published without the author’s prior written consent. Abstract

This research project focuses primarily on the development of orthoface for recognition. The orthoface method is a novel face recognition algorithm. Orthoface method transforms faces from image space to face space. It achieves a dimension reduction similar to that of eigenface. All of the classification methods applicable to eigenface method can be applied to the orthoface method without any further modification. It maximises the inter-class scattering, and minimizes the intra-class scattering, which has been the weakness of the conventional eigenface method.

The project matrix of the training face from the orthoface method forms an upper-triangular matrix. Each training face has a different number of coefficients allowing better discrimination and classification. A new classification technique is applied, in addition to the supports for the traditional classifiers. The new classification technique does not require comparison between test face coefficients and training face coefficients. This improves recognition speed significantly. It is estimated conservatively that such an improvement is no less than 10 times faster.

Using the basic orthoface method, the recognition rate is 87%. By increasing to 3 training faces per subject, the recognition rate improves to 92%. However, using hand-picked difficult test faces, a recognition rate of 83% is achieved. This is further improved using orthospace histogram method, thus yielding a 97% recognition rate.

The second, less significant, part of this research project focuses on pose-invariant face recognition. This part concentrates on the use of a 3D head model and texture mapping technique to derive new pose views from one or two existing views. The complete model is discussed in depth. The idea of using one generic 3D head model for mapping facial texture is realised through the use of a deformable 3D head model. Deformation is done via parameters extracted from various feature measurements. The facial texture is mapped onto the 3D surface using cylinder texture mapping. The mathematics to compute the vertex on the cylinder is derived and presented in detail.


I would like to thank my two supervisors, Dr. Bai Li and Professor Dave Elliman for their continuous support, supervision, and inspiring ideas over the course of this research project. Without their guidance, this research project will not be completed.

Dr. Tony Pridmore for his critical evaluation of my research ideas.

My research associates, Mr. Jerry Ni, Miss Y. H. Liu for their inspiring suggestions and enormous amount of time to discuss my research problems.

Miss. P. S. Theng for proof reading my thesis.

My family and girlfriend Miranda for their continuous support and encouragement.

My friend Kenny Liew, David Leong, K. H. Wong their mental support.

Table of Content



List of Figuresv

List of Tablesvi


1.1Face Recognition In Perspective1
1.1.1What is Face Recognition1
1.1.2Why Face Recognition is Important2
1.1.3Application of Face Recognition3
1.1.4Face Recognition from the Psychological Perspective6 1.1.5Face Recognition as a Computer Science Problem9 1.1.6Biometric – the Future of Security Authentication11...
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